OpenRaiser/Intern-Atlas huggingface dataset:
Secret Key Generation Based on AoA Estimation for Low SNR Conditions
Venue: arXiv cs.IT 2014
Year: 2014

In the context of physical layer security, a physical layer characteristic is used as a common source of randomness to generate the secret key. Therefore an accurate estimation of this characteristic is the core for reliable secret key generation. Estimation of almost all the existing physical layer characteristic suffer dramatically at low signal to noise (SNR) levels. In this paper, we propose a novel secret key generation algorithm that is based on the estimated angle of arrival (AoA) between the two legitimate nodes. Our algorithm has an outstanding performance at very low SNR levels. Our algorithm can exploit either the Azimuth AoA to generate the secret key or both the Azimuth and Elevation angles to generate the secret key. Exploiting a second common source of randomness adds an extra degree of freedom to the performance of our algorithm. We compare the performance of our algorithm to the algorithm that uses the most commonly used characteristics of the physical layer which are channel amplitude and phase. We show that our algorithm has a very low bit mismatch rate (BMR) at very low SNR when both channel amplitude and phase based algorithm fail to achieve an acceptable BMR.
Designing high-speed, low-power full adder cells based on carbon nanotube technology
Venue: arXiv cs.AR 2014
Year: 2014

This article presents novel high speed and low power full adder cells based on carbon nanotube field effect transistor (CNFET). Four full adder cells are proposed in this article. First one (named CN9P4G) and second one (CN9P8GBUFF) utilizes 13 and 17 CNFETs respectively. Third design that we named CN10PFS uses only 10 transistors and is full swing. Finally, CN8P10G uses 18 transistors and divided into two modules, causing Sum and Cout signals are produced in a parallel manner. All inputs have been used straight, without inverting. These designs also used the special feature of CNFET that is controlling the threshold voltage by adjusting the diameters of CNFETs to achieve the best performance and right voltage levels. All simulation performed using Synopsys HSPICE software and the proposed designs are compared to other classical and modern CMOS and CNFET-based full adder cells in terms of delay, power consumption and power delay product.
Independent sets and hitting sets of bicolored rectangular families
Venue: arXiv cs.CG 2014
Year: 2014

A bicolored rectangular family BRF is a collection of all axis-parallel rectangles contained in a given region Z of the plane formed by selecting a bottom-left corner from a set A and an upper-right corner from a set B. We prove that the maximum independent set and the minimum hitting set of a BRF have the same cardinality and devise polynomial time algorithms to compute both. As a direct consequence, we obtain the first polynomial time algorithm to compute minimum biclique covers, maximum cross-free matchings and jump numbers in a class of bipartite graphs that significantly extends convex bipartite graphs and interval bigraphs. We also establish several connections between our work and other seemingly unrelated problems. Furthermore, when the bicolored rectangular family is weighted, we show that the problem of finding the maximum weight of an independent set is NP-hard, and provide efficient algorithms to solve it on certain subclasses.
Capacity Achieving Peak Power Limited Probability Measures: Sufficient Conditions for Finite Discreteness
Venue: arXiv cs.IT 2014
Year: 2014

The problem of capacity achieving (optimal) input probability measures has been widely investigated for several channel models with constrained inputs. So far, no outstanding generalizations have been derived. This paper does a forward step in this direction, by introducing a set of new requirements, for the class of real scalar conditional output probability measures, under which the optimal input probability measure is shown to be discrete with a finite number of probability mass points, when peak power limited.
Unshared Secret Key Cryptography: Finite Constellation Inputs and Ideal Secrecy Outage
Venue: arXiv cs.CR 2014
Year: 2014

The Unshared Secret Key Cryptography (USK), recently proposed by the authors, guarantees Shannon's ideal secrecy and perfect secrecy for MIMO wiretap channels, without requiring secret key exchange. However, the requirement of infinite constellation inputs limits its applicability to practical systems. In this paper, we propose a practical USK scheme using finite constellation inputs. The new scheme is based on a cooperative jamming technique, and is valid for the case where the eavesdropper has more antennas than the transmitter. We show that Shannon's ideal secrecy can be achieved with an arbitrarily small outage probability.
NAVI: Neighbor Aware Virtual Infrastructure for Information Dissemination in Vehicular Networks
Venue: arXiv cs.NI 2014
Year: 2014

Vehicular Networks enable a vast number of innovative applications, which rely on the efficient exchange of information between vehicles. However, efficient and reliable data dissemination is a particularly challenging task in the context of vehicular networks due to the underlying properties of these networks, limited availability of network infrastructure and variable penetration rates for distinct communication technologies. This paper presents a novel system and mechanism for information dissemination based on virtual infrastructure selection in combination with multiple communication technologies. The system has been evaluated using a simulation framework, involving network simulation in conjugation with realistic vehicular mobility traces. The presented simulation results show the feasibility of the proposed mechanism to achieve maximum message penetration with reduced overhead. Compared with a cellular-based only solution, our mechanism shows that the judicious vehicle selection can lead to improved network utilization through the offload of traffic to the short-range communication network.
Interference Alignment: Practical Challenges and Test-bed Implementation
Venue: arXiv cs.IT 2014
Year: 2014

Data traffic over wireless communication networks has experienced a tremendous growth in the last decade, and it is predicted to exponentially increase in the next decades. Enabling future wireless networks to fulfill this expectation is a challenging task both due to the scarcity of radio resources (e.g. spectrum and energy), and also the inherent characteristics of the wireless transmission medium. Wireless transmission is in general subject to two phenomena: fading and interference. The elegant interference alignment concept reveals that with proper transmission signalling design, different interference signals can in fact be aligned together, such that more radio resources can be assigned to the desired transmission. Although interference alignment can achieve a larger data rate compared to orthogonal transmission strategies, several challenges should be addressed to enable the deployment of this technique in future wireless networks For instance, to perform interference alignment, normally, global channel state information (CSI) is required to be perfectly known at all terminals. Clearly, acquiring such channel knowledge is a challenging problem in practice and proper channel training and channel state feedback techniques need to be deployed. In addition, since the channels are time-varying proper adaptive transmission is needed. This chapter review recent advances in practical aspects of interference alignment. It also presents recent test-bed implementations of signal processing algorithms for the realization of interference alignment.
Branch-and-Reduce Exponential/FPT Algorithms in Practice: A Case Study of Vertex Cover
Venue: arXiv cs.DS 2014
Year: 2014

We investigate the gap between theory and practice for exact branching algorithms. In theory, branch-and-reduce algorithms currently have the best time complexity for numerous important problems. On the other hand, in practice, state-of-the-art methods are based on different approaches, and the empirical efficiency of such theoretical algorithms have seldom been investigated probably because they are seemingly inefficient because of the plethora of complex reduction rules. In this paper, we design a branch-and-reduce algorithm for the vertex cover problem using the techniques developed for theoretical algorithms and compare its practical performance with other state-of-the-art empirical methods. The results indicate that branch-and-reduce algorithms are actually quite practical and competitive with other state-of-the-art approaches for several kinds of instances, thus showing the practical impact of theoretical research on branching algorithms.
Opportunistic Multicast Scheduling for Unicast Transmission in MIMO-OFDM System
Venue: arXiv cs.NI 2014
Year: 2014

We propose a multicast scheduling scheme to exploit content reuse when there is asynchronicity in user requests. A unicast transmission setup is used for content delivery, while multicast transmission is employed opportunistically to reduce wireless resource usage. We then develop a multicast scheduling scheme for the downlink multiple-input multiple output orthogonal-frequency division multiplexing system in IEEE 802.11 wireless local area network (WLAN). At each time slot, the scheduler serves the users by either unicast or multicast transmission. Out-sequence data received by a user is stored in user's cache for future use.Multicast precoding and user selection for multicast grouping are also considered and compliance with the IEEE 802.11 WLAN transmission protocol. The scheduling scheme is based on the Lyapunov optimization technique, which aims to maximize system rate. The resulting scheme has low complexity and requires no prior statistical information on the channels and queues. Furthermore, in the absence of channel error, the proposed scheme restricts the worst case of frame dropping deadline, which is useful for delivering real-time traffic. Simulation results show that our proposed algorithm outperforms existing techniques by 17 % to 35 % in term of user capacity.
Generator kodow liniowych o skonczonych charakterystykach
Venue: arXiv cs.IT 2014
Year: 2014

The paper presents a method to generate some families of linear codes over finite fields of characteristics greater than two in the widest class due to the size of Grassmann manifold, i.e. when the dimension is equal to codimension. Our method applies some simple embedding of projective line into the Grassman manifold.
Easy/Hard Transition in k-SAT
Venue: arXiv cs.CC 2014
Year: 2014

A heuristic model procedure for determining satisfiability of CNF-formulae is set up and described by nonlinear recursion relations for m (number of clauses), n (number of variables) and clause filling k. The system mimicked by the recursion undergoes a sharp transition from bounded running times (easy) to uncontrolled runaway behaviour (hard). Thus the parameter space turns out to be separated into regions with qualitatively different efficiency of the model procedure. The transition results from a competition of exponential blow up by branching versus growing number of orthogonal clauses.
Fast Prefix Adders for Non-Uniform Input Arrival Times
Venue: arXiv cs.AR 2014
Year: 2014

We consider the problem of constructing fast and small parallel prefix adders for non-uniform input arrival times. This problem arises whenever the adder is embedded into a more complex circuit, e. g. a multiplier. Most previous results are based on representing binary carry-propagate adders as so-called parallel prefix graphs, in which pairs of generate and propagate signals are combined using complex gates known as prefix gates. Adders constructed in this model usually minimize the delay in terms of these prefix gates. However, the delay in terms of logic gates can be worse by a factor of two. In contrast, we aim to minimize the delay of the underlying logic circuit directly. We prove a lower bound on the delay of a carry bit computation achievable by any prefix carry bit circuit and develop an algorithm that computes a prefix carry bit circuit with optimum delay up to a small additive constant. Furthermore, we use this algorithm to construct a small parallel prefix adder. Compared to existing algorithms we simultaneously improve the delay and size guarantee, as well as the running time for constructing prefix carry bit and adder circuits.
Application Layer Intrusion Detection with Combination of Explicit-Rule- Based and Machine Learning Algorithms and Deployment in Cyber- Defence Program
Venue: arXiv cs.CR 2014
Year: 2014

There have been numerous works on network intrusion detection and prevention systems, but work on application layer intrusion detection and prevention is rare and not very mature. Intrusion detection and prevention at both network and application layers are important for cyber-security and enterprise system security. Since application layer intrusion is increasing day by day, it is imperative to give adequate attention to it and use state-of-the-art algorithms for effective detection and prevention. This paper talks about current state of application layer intrusion detection and prevention capabilities in commercial and open-source space and provides a path for evolution to more mature state that will address not only enterprise system security, but also national cyber-defence. Scalability and cost-effectiveness were important factors which shaped the proposed solution.
Prefrontal Cortex Motivated Cognitive Architecture for Multiple Robots
Venue: arXiv cs.RO 2014
Year: 2014

In this paper, we introduce a cerebral cortex inspired architecture for robots in which we have mapped hierarchical cortical representation of human brain to logic flow and decision making process. Our work focuses on the two major features of human cognitive process, viz. the perception action cycle and its hierarchical organization, and the decision making process. To prove the effectiveness of our proposed method, we incorporated this architecture in our robot which we named as Cognitive Insect Robot inspired by Brain Architecture (CIRBA). We have extended our research to the implementation of this cognitive architecture of CIRBA in multiple robots and have analyzed the level of cognition attained by them
Distributed Representations for Compositional Semantics
Venue: arXiv cs.CL 2014
Year: 2014

The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches --- meaning distributed representations that exploit co-occurrence statistics of large corpora --- have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.
Sparse Modeling for Image and Vision Processing
Venue: arXiv cs.CV 2014
Year: 2014

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
Efficiency of Matrix Multiplication on the Cross-Wired Mesh Array
Venue: arXiv cs.DC 2014
Year: 2014

This note looks at the efficiency of the cross-wired mesh array in the context of matrix multiplication. It is shown that in case of repeated operations, the average number of steps to multiply sets of nxn matrices on a 2D cross-wired mesh array approaches n.
Membership Function Assignment for Elements of Single OWL Ontology
Venue: arXiv cs.AI 2014
Year: 2014

This paper develops the idea of membership function assignment for OWL (Web Ontology Language) ontology elements in order to subsequently generate fuzzy rules from this ontology. The task of membership function assignment for OWL ontology elements had already been partially described, but this concerned the case, when several OWL ontologies of the same domain were available, and they were merged into a single ontology. The purpose of this paper is to present the way of membership function assignment for OWL ontology elements in the case, when there is the only one available ontology. Fuzzy rules, generated from the OWL ontology, are necessary for supplement of the SWES (Semantic Web Expert System) knowledge base. SWES is an expert system, which will be able to extract knowledge from OWL ontologies, found in the Web, and will serve as a universal expert for the user.
Comprehensive and Reliable Crowd Assessment Algorithms
Venue: arXiv cs.DB 2014
Year: 2014

Evaluating workers is a critical aspect of any crowdsourcing system. In this paper, we devise techniques for evaluating workers by finding confidence intervals on their error rates. Unlike prior work, we focus on "conciseness"---that is, giving as tight a confidence interval as possible. Conciseness is of utmost importance because it allows us to be sure that we have the best guarantee possible on worker error rate. Also unlike prior work, we provide techniques that work under very general scenarios, such as when not all workers have attempted every task (a fairly common scenario in practice), when tasks have non-boolean responses, and when workers have different biases for positive and negative tasks. We demonstrate conciseness as well as accuracy of our confidence intervals by testing them on a variety of conditions and multiple real-world datasets.
High gain two-stage amplifier with positive capacitive feedback compensation
Venue: arXiv cs.OH 2014
Year: 2014

A novel topology for a high gain two-stage amplifier is proposed. The proposed circuit is designed in a way that the non-dominant pole is at output of the first stage. A positive capacitive feedback (PCF) around the second stage introduces a left half plane (LHP) zero which cancels the phase shift introduced by the non-dominant pole, considerably. The dominant pole is at the output node which means that increasing the load capacitance has minimal effect on stability. Moreover, a simple and effective method is proposed to enhance slew rate. Simulation shows that slew rate is improved by a factor of 2.44 using the proposed method. The proposed amplifier is designed in a 0.18um CMOS process. It consumes 0.86mW power from a 1.8V power supply and occupies 3038.5um2 of chip area. The DC gain is 82.7dB and gain bandwidth (GBW) is 88.9 MHz when driving a 5pF capacitive load. Also low frequency CMRR and PSRR+ are 127dB and 83.2dB, respectively. They are 24.8dB and 24.2dB at GBW frequency, which are relatively high and are other important properties of the proposed amplifier. Moreover, Simulations show convenient performance of the circuit in process corners and also presence of mismatch.
Gaze Stabilization for Humanoid Robots: a Comprehensive Framework
Venue: arXiv cs.RO 2014
Year: 2014

Gaze stabilization is an important requisite for humanoid robots. Previous work on this topic has focused on the integration of inertial and visual information. Little attention has been given to a third component, which is the knowledge that the robot has about its own movement. In this work we propose a comprehensive framework for gaze stabilization in a humanoid robot. We focus on the problem of compensating for disturbances induced in the cameras due to self-generated movements of the robot. In this work we employ two separate signals for stabilization: (1) an anticipatory term obtained from the velocity commands sent to the joints while the robot moves autonomously; (2) a feedback term from the on board gyroscope, which compensates unpredicted external disturbances. We first provide the mathematical formulation to derive the forward and the differential kinematics of the fixation point of the stereo system. We finally test our method on the iCub robot. We show that the stabilization consistently reduces the residual optical flow during the movement of the robot and in presence of external disturbances. We also demonstrate that proper integration of the neck DoF is crucial to achieve correct stabilization.
Quantification of reachable attractors in asynchronous discrete dynamics
Venue: arXiv cs.DM 2014
Year: 2014

Motivation: Models of discrete concurrent systems often lead to huge and complex state transition graphs that represent their dynamics. This makes difficult to analyse dynamical properties. In particular, for logical models of biological regulatory networks, it is of real interest to study attractors and their reachability from specific initial conditions, i.e. to assess the potential asymptotical behaviours of the system. Beyond the identification of the reachable attractors, we propose to quantify this reachability. Results: Relying on the structure of the state transition graph, we estimate the probability of each attractor reachable from a given initial condition or from a portion of the state space. First, we present a quasi-exact solution with an original algorithm called Firefront, based on the exhaustive exploration of the reachable state space. Then, we introduce an adapted version of Monte Carlo simulation algorithm, termed Avatar, better suited to larger models. Firefront and Avatar methods are validated and compared to other related approaches, using as test cases logical models of synthetic and biological networks. Availability: Both algorithms are implemented as Perl scripts that can be freely downloaded from http://compbio.igc.gulbenkian.pt/nmd/node/59 along with Supplementary Material.